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Neural networks model and embedded stochastic processes for hydrological analysis in South Korea

  • Water Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

The Spatial-Stochastic Neural Networks Model (SSNNM) is used to estimate long-term streamflow in parallel reservoir groups. SSNNM employs two kinds of backpropagation algorithms, separately based on LMBP and BFGS-QNBP. SSNNM has three layers in the structure-input, hidden, and output. The network configuration consists of 8-8-2 nodes in each one. Nodes in the input layer are composed of streamflow, precipitation, evaporation, and temperature with monthly average values collected from the Andong and Imha reservoirs. But some temporal differences apparently exist in their time series. For the SSNNM training procedure, the training sets in the input layer are generated by the PARMA (1,1) stochastic model and cover insufficient time series. Generated data series are used to train SSNNM and the model parameters, optimal connection weights and biases estimated during training procedure. They evaluate model validation using observed data sets. In this study, by comparing statistical analysis and hydrographs in the model validation, we find that the new approaches give outstanding results. SSNNM will help manage and control water distribution and provide basic data to develop a long-term coupled operation system in parallel reservoir groups of the upper Nakdong River, South Korea.

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  1. School of Architectural Design and Environmental Engineering, Dongyang University, Korea

    Sungwon Kim (Member, Assistant Professor)

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  1. Sungwon Kim
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Correspondence to Sungwon Kim.

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Kim, S. Neural networks model and embedded stochastic processes for hydrological analysis in South Korea. KSCE J Civ Eng 8, 141–148 (2004). https://doi.org/10.1007/BF02829090

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  • DOI: https://doi.org/10.1007/BF02829090

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